Triple
T6738367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | East Orange, New Jersey |
E154013
|
entity |
| Predicate | distanceToNewYorkCityInMiles |
P2383
|
FINISHED |
| Object | approximately 13 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: approximately 13 | Statement: [East Orange, New Jersey, distanceToNewYorkCityInMiles, approximately 13]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToNewYorkCityInMiles Context triple: [East Orange, New Jersey, distanceToNewYorkCityInMiles, approximately 13]
-
A.
distanceToNewYorkCity
chosen
Indicates the spatial distance between a given entity’s location and New York City.
-
B.
distanceToNewYorkCityApproxKm
Indicates the approximate physical distance, measured in kilometers, between a given entity’s location and New York City.
-
C.
approximateGreatCircleDistanceMiles
Indicates the approximate distance, measured in miles, between two locations along the great-circle path on the surface of a sphere (typically the Earth).
-
D.
distanceToBaltimoreInMiles
Indicates the numerical distance, measured in miles, between a given location and the city of Baltimore.
-
E.
distanceFromMiami
Indicates the spatial distance between a given entity’s location and the city of Miami.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c6880d84d8819095d19de2295f26ac |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d1866dbc81909483fbd5ed6a3ec8 |
completed | March 27, 2026, 6:50 p.m. |
| PD | Predicate disambiguation | batch_69c6d09067a0819087ed6c820f4699f8 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:10 p.m.